Lidé

Ing. Patrik Vacek

Všechny publikace

Real3D-Aug: Point Cloud Augmentation by Placing Real Objects with Occlusion Handling for 3D Detection and Segmentation

  • Pracoviště: Vidění pro roboty a autonomní systémy
  • Anotace:
    Object detection and semantic segmentation with the 3D LiDAR point cloud data require expensive annotation. We propose a data augmentation method that takes advantage of already annotated data multiple times. We propose an augmentation framework that reuses real data, automatically finds suitable placements in the scene to be augmented, and handles occlusions explicitly. Due to the usage of the real data, the scan points of newly inserted objects in augmentation sustain the physical characteristics of the LiDAR, such as intensity and raydrop. The pipeline proves competitive in training top-performing models for 3D object detection and semantic segmentation. The new augmentation provides a significant performance gain in rare and essential classes, notably 6.65% average precision gain for “Hard” pedestrian class in KITTI object detection or 2.14 mean IoU gain in the SemanticKITTI segmentation challenge over the state of the art.

Teachers in Concordance for Pseudo-Labeling of 3D Sequential Data

  • DOI: 10.1109/LRA.2022.3226029
  • Odkaz: https://doi.org/10.1109/LRA.2022.3226029
  • Pracoviště: Vidění pro roboty a autonomní systémy
  • Anotace:
    Automatic pseudo-labeling is a powerful tool to tap into large amounts of sequential unlabeled data. It is especially appealing in safety-critical applications of autonomous driving, where performance requirements are extreme, datasets are large, and manual labeling is very challenging. We propose to leverage sequences of point clouds to boost the pseudo-labeling technique in a teacher-student setup via training multiple teachers, each with access to different temporal information. This set of teachers, dubbed Concordance , provides higher quality pseudo-labels for student training than standard methods. The output of multiple teachers is combined via a novel pseudo-label confidence-guided criterion. Our experimental evaluation focuses on the 3D point cloud domain and urban driving scenarios. We show the performance of our method applied to 3D semantic segmentation and 3D object detection on three benchmark datasets. Our approach, which uses only 20% manual labels, outperforms some fully supervised methods. A notable performance boost is achieved for classes rarely appearing in training data. Our codes will be made publicly available.

Learning to Predict Lidar Intensities

  • DOI: 10.1109/TITS.2020.3037980
  • Odkaz: https://doi.org/10.1109/TITS.2020.3037980
  • Pracoviště: Vidění pro roboty a autonomní systémy
  • Anotace:
    We propose a data-driven method for simulating lidar sensors. The method reads computer-generated data, and (i) extracts geometrically simulated lidar point clouds and (ii) predicts the strength of the lidar response – lidar intensities. Qualitative valuation of the proposed pipeline demonstrates the ability to predict systematic ailures such as no/low responses on polished parts of car bodyworks and windows, for strong responses on reflective surfaces such as traffic signs and license/registration plates. We also experimentally show that enhancing the training set by such simulated data improves the segmentation accuracy on the real dataset with limited access to real data. Implementation of the resulting lidar simulator for the GTA V game, as well as the accompanying large dataset, is made publicly available.

Za stránku zodpovídá: Ing. Mgr. Radovan Suk